In RDBN, SVM with RBF kernel (RBF-SVM) is selected to attach DBN.
To further validate RDBN, many methods are compared on the speech emotion databases.
Finally, RDBN obtains the accuracies higher than L-SVM by 1.13%, 6.42%, and 7.35%, respectively, on three databases.
Hence instead of WA, UA is applied to evaluate RDBN, where its optimal parameters are determined in advance through experiments.
Secondly, RDBN outperforms BASE by 3.1%, illustrating that the ensemble learning is definitely superior to its single classifier.
It can be concluded from the above experimental results that RDBN consistently outperforms DBN, SVM, and KNN for speech emotion recognition.
This paper presents a random deep belief network (RDBN) ensemble method for speech emotion recognition.
(5) Both the RDBN and RCB systems require absence of invasive carcinoma in both breast and lymph nodes.
(25,31,36) In RDBN, high tumor grade weighs in as a negative prognostic factor when residual tumor is present, in addition to lymph node involvement and tumor size.
In this study, RDBN was associated with clinical outcome by univariate and multivariate analysis.
Residual Disease in Breast and Nodes (RDBN) response in relation to distant disease-free survival (A) and overall survival (B) by age-adjusted Kaplan-Meier survival analysis.
A new prognostic classification after primary chemotherapy for breast cancer: residual disease in breast and nodes (RDBN).